Inertial navigation is the process of estimating the
traveled distance and orientation of an object by time-integration of
measured velocities and accelerations [1]}. Due to the
integrative nature of inertial navigation, the estimated
position and orientation do inherently accumulate errors over
time. This unbounded accumulation of position and
orientation errors can only be limited by including external information
from other sensor systems that provide an absolute reference to the
environment or by including motion constraints. For instance, such aided
inertial navigation has been done using
satellite [2]}, video [3]},
radio [4]}, LIDAR [5]}, and magnetic
field data [6]}. Otherwise, the position and
orientation errors' growth rate can be
reduced
by decreasing the measurement errors of the inertial sensors or using
additional motion information such as
ZUPT [7]}. Improving the
sensor hardware [8]} reduces
measurement errors, but this typically comes with increasing cost and
sensor size. Another approach to reducing the measurement error is to
fuse the measurements from a redundant amount
of inertial sensors to produce a virtual sensor with higher accuracy. In
particular, small MEMS-based inertial sensors can be
cheaply fabricated [9]} and are therefore suited for
the construction of such sensor arrays [10]}, [11]}.
